This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. A key lesson learned from the 2009-2010 influenza pandemic was the importance of being prepared for viruses that jump from animal species by developing the ability to bind the sialoglycans of the human airway. Toward this end, the CDC has been using glycan arrays to analyze the binding specificity of influenza viruses from various animal species. Glycan array data, however, tends to be difficult to interpret because it provides only apparent relative affinities for binding to a given protein, and the glycan binding results are often riddled with false positives and negatives. This project aims to develop the bioinformatic tools necessary to improve analysis of glycan array data and reduce the time needed to convert laboratory bench results into actionable information for public health decision-makers. In addition, the tools may be employed in veterinary medicine for various aspects of influenza surveillance. The proposed bioinformatic tools will be capable of analyzing glycan array data to identify true binders, their minimal motifs, and binding affinity, as well as performing structural assessment to elucidate the basis for binding or non-binding. The ability to rapidly translate glycan array data into concise, practical information will greatly improve the CDC's ability to provide influenza risk assessment. Researchers would gain the ability to screen a given influenza virus and immediately report its specificity based on the types of ligands it binds and assess its infectivity based on relative binding affinity.